Customer order fulfillment at distribution centers (DC) is increasingly necessitated by innovative strategies to maximize operational performance that are primarily driven by cost and service level under supply chain variability. In order to better understand the trade-offs, in this paper, a generic computational model is developed to estimate forklift travel times for DCs with any arbitrary floor space and loading docks. In particular, travel times are modelled as random variables and the moments of the probability distribution of travel times are estimated and used as inputs to analytical queueing model and discrete event simulation model. Results show that the analytical and simulation models are within 3% under different demand scenarios. These models are used to determine the impact of work-force capacity on key performance measures such as Truck Processing Time (TPT) and Labor Hours Per Truck (LHPT). The workforce capacity for different demand scenarios is determined using three different approaches - Target Utilization Level, Square Root Staffing (SRS) rule (adapted from call center staffing) and Optimization. The result from these models indicate that adapting workforce capacity to match varying demand can reduce cost by 18% while maintaining desired service level.